The prevalence and perniciousness of fake news has been a critical issue on
the Internet, which stimulates the development of automatic fake news detection
in turn. In this paper, we focus on the evidence-based fake news detection,
where several evidences are utilized to probe the veracity of news (i.e., a
claim). Most previous methods first employ sequential models to embed the
semantic information and then capture the claim-evidence interaction based on
different attention mechanisms. Despite their effectiveness, they still suffer
from two main weaknesses. Firstly, due to the inherent drawbacks of sequential
models, they fail to integrate the relevant information that is scattered far
apart in evidences for veracity checking. Secondly, they neglect much redundant
information contained in evidences that may be useless or even harmful. To
solve these problems, we propose a unified Graph-based sEmantic sTructure
mining framework, namely GET in short. Specifically, different from the
existing work that treats claims and evidences as sequences, we model them as
graph-structured data and capture the long-distance semantic dependency among
dispersed relevant snippets via neighborhood propagation. After obtaining
contextual semantic information, our model reduces information redundancy by
performing graph structure learning. Finally, the fine-grained semantic
representations are fed into the downstream claim-evidence interaction module
for predictions. Comprehensive experiments have demonstrated the superiority of
GET over the state-of-the-arts.

本研究提出了一种基于证据的假新闻自动检测框架 GET，该框架采用图形结构学习方法将索证和索证的长距离语义依赖关系整合起来，从而有效避免了序列模型在验证新闻真实性方面存在的缺陷。作为一项重要的技术创新，GET 的有效性已经得到了充分验证。